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July  2015, 11(3): 921-932. doi: 10.3934/jimo.2015.11.921

Clustering based polyhedral conic functions algorithm in classification

Gurkan Ozturk 1, and  Mehmet Tahir Ciftci 2,

1. 

Department of Industrial Engineering, Faculty of Engineering, Anadolu University, Eskisehir, 26555, Turkey
2. 

Vitra, Eczacibasi Yapi Gerecleri, 11300 Bilecik, Turkey

Received  October 2013 Revised  June 2014 Published  October 2014

In this study, a new algorithm based on polyhedral conic functions (PCFs) is developed to solve multi-class supervised data classification problems. The $k$ PCFs are constructed for each class in order to separate it from the rest of the data set. The $k$-means algorithm is applied to find vertices of PCFs and then a linear programming model is solved to calculate the parameters of each PCF. The separating functions for each class are obtained as a pointwise minimum of the PCFs. A class label is assigned to the test point according to its minimum value over all separating functions. In order to demonstrate the performance of the proposed algorithm, it is applied to solve classification problems in publicly available data sets. The comparative results with some mainstream classifiers are presented.
Keywords: computational learning theory., polyhedral conic cunctions, Classification, linear programming, K-means.
Mathematics Subject Classification: Primary: 68Q32, 46N10; Secondary: 97R5.
Citation: Gurkan Ozturk, Mehmet Tahir Ciftci. Clustering based polyhedral conic functions algorithm in classification. Journal of Industrial & Management Optimization, 2015, 11 (3) : 921-932. doi: 10.3934/jimo.2015.11.921
References:
[1]

A. Astorino and M. Gaudioso, Polyhedral separability through successive LP, Journal of Optimization Theory and Applications, 112 (2002), 265-293. doi: 10.1023/A:1013649822153.  Google Scholar

[2]

A. Astorino, M. Gaudioso and A. Seeger, Conic separation of finite sets. i: The homogeneous case, Journal of Convex Analysis, 21 (2014), 001-028. Google Scholar

[3]

K. Bache and M. Lichman, UCI machine learning repository, 2013., URL , ().   Google Scholar

[4]

A. M. Bagirov, Max-min separability, Optimization Methods and Software, 20 (2005), 277-296. doi: 10.1080/10556780512331318263.  Google Scholar

[5]

A. M. Bagirov and J. Ugon, Supervised data classification via max-min separability, Applied Optimization, 99 (2005), 175-207. doi: 10.1007/0-387-26771-9_6.  Google Scholar

[6]

A. M. Bagirov, M. Ghosh and D. Webb, A derivative-free method for linearly constrained nonsmooth optimization, Journal of Industrial and Management Optimization, 2 (2006), 319-338. Google Scholar

[7]

A. M. Bagirov, J. Ugon, D. Webb, G. Ozturk and R. Kasimbeyli, A novel piecewise linear classifier based on polyhedral conic and max-min separabilities, TOP, 21 (2013), 3-24. ISSN 1134-5764. doi: 10.1007/s11750-011-0241-5.  Google Scholar

[8]

C. J. C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2 (1998), 121-167. Google Scholar

[9]

R. N. Gasimov and G. Ozturk, Separation via polihedral conic functions, Optimization Methods and Software, 21 (2006), 527-540. doi: 10.1080/10556780600723252.  Google Scholar

[10]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, The weka data mining software: An update, SIGKDD Explorations, 11 (2009), 10-18. ISSN 1931-0145. doi: 10.1145/1656274.1656278.  Google Scholar

[11]

R. Kasimbeyli, Radial epiderivatives and set-valued optimization, Optimization, 58 (2009), 521-534. doi: 10.1080/02331930902928310.  Google Scholar

[12]

R. Kasimbeyli, A nonlinear cone separation theorem and scalarization in nonconvex vector optimization, SIAM J. on Optimization, 20 (2009), 1591-1619. ISSN 1052-6234. doi: 10.1137/070694089.  Google Scholar

[13]

R. Kasimbeyli and M. Mammadov, On weak subdifferentials, directional derivatives, and radial epiderivatives for nonconvex functions, SIAM Journal on Optimization, 20 (2009), 841-855. doi: 10.1137/080738106.  Google Scholar

[14]

G. Ozturk, A New Mathematical Programming Approach to Solve Classification Problems, PhD thesis, Eskisehir Osmangazi University, Institute of Scince, 6 2007. (in Turkish). Google Scholar

[15]

R. Rosenthal, GAMS: A User's Guide, GAMS Development Corporation, Washington, DC, 2013. URL http://www.gams.com/dd/docs/bigdocs/GAMSUsersGuide.pdf. Google Scholar

[16]

K. Schittkowski, Optimal parameter selection in support vector machines, Journal of Industrial and Management Optimization, 1 (2005), 465-476. doi: 10.3934/jimo.2005.1.465.  Google Scholar

show all references

References:
[1]

A. Astorino and M. Gaudioso, Polyhedral separability through successive LP, Journal of Optimization Theory and Applications, 112 (2002), 265-293. doi: 10.1023/A:1013649822153.  Google Scholar

[2]

A. Astorino, M. Gaudioso and A. Seeger, Conic separation of finite sets. i: The homogeneous case, Journal of Convex Analysis, 21 (2014), 001-028. Google Scholar

[3]

K. Bache and M. Lichman, UCI machine learning repository, 2013., URL , ().   Google Scholar

[4]

A. M. Bagirov, Max-min separability, Optimization Methods and Software, 20 (2005), 277-296. doi: 10.1080/10556780512331318263.  Google Scholar

[5]

A. M. Bagirov and J. Ugon, Supervised data classification via max-min separability, Applied Optimization, 99 (2005), 175-207. doi: 10.1007/0-387-26771-9_6.  Google Scholar

[6]

A. M. Bagirov, M. Ghosh and D. Webb, A derivative-free method for linearly constrained nonsmooth optimization, Journal of Industrial and Management Optimization, 2 (2006), 319-338. Google Scholar

[7]

A. M. Bagirov, J. Ugon, D. Webb, G. Ozturk and R. Kasimbeyli, A novel piecewise linear classifier based on polyhedral conic and max-min separabilities, TOP, 21 (2013), 3-24. ISSN 1134-5764. doi: 10.1007/s11750-011-0241-5.  Google Scholar

[8]

C. J. C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2 (1998), 121-167. Google Scholar

[9]

R. N. Gasimov and G. Ozturk, Separation via polihedral conic functions, Optimization Methods and Software, 21 (2006), 527-540. doi: 10.1080/10556780600723252.  Google Scholar

[10]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, The weka data mining software: An update, SIGKDD Explorations, 11 (2009), 10-18. ISSN 1931-0145. doi: 10.1145/1656274.1656278.  Google Scholar

[11]

R. Kasimbeyli, Radial epiderivatives and set-valued optimization, Optimization, 58 (2009), 521-534. doi: 10.1080/02331930902928310.  Google Scholar

[12]

R. Kasimbeyli, A nonlinear cone separation theorem and scalarization in nonconvex vector optimization, SIAM J. on Optimization, 20 (2009), 1591-1619. ISSN 1052-6234. doi: 10.1137/070694089.  Google Scholar

[13]

R. Kasimbeyli and M. Mammadov, On weak subdifferentials, directional derivatives, and radial epiderivatives for nonconvex functions, SIAM Journal on Optimization, 20 (2009), 841-855. doi: 10.1137/080738106.  Google Scholar

[14]

G. Ozturk, A New Mathematical Programming Approach to Solve Classification Problems, PhD thesis, Eskisehir Osmangazi University, Institute of Scince, 6 2007. (in Turkish). Google Scholar

[15]

R. Rosenthal, GAMS: A User's Guide, GAMS Development Corporation, Washington, DC, 2013. URL http://www.gams.com/dd/docs/bigdocs/GAMSUsersGuide.pdf. Google Scholar

[16]

K. Schittkowski, Optimal parameter selection in support vector machines, Journal of Industrial and Management Optimization, 1 (2005), 465-476. doi: 10.3934/jimo.2005.1.465.  Google Scholar

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